---
library_name: transformers
license: mit
base_model: intfloat/e5-small-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: e5-small-v2-sentiment-twitter
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# e5-small-v2-sentiment-twitter

This model is a fine-tuned version of [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6420
- Accuracy: 0.7191
- F1: 0.7184
- Precision: 0.7232
- Recall: 0.7191

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7497        | 0.1754 | 500  | 0.7109          | 0.699    | 0.6941 | 0.7013    | 0.699  |
| 0.6768        | 0.3508 | 1000 | 0.6320          | 0.7225   | 0.7229 | 0.7242    | 0.7225 |
| 0.633         | 0.5261 | 1500 | 0.6572          | 0.706    | 0.7110 | 0.7340    | 0.706  |
| 0.6566        | 0.7015 | 2000 | 0.6138          | 0.7235   | 0.7179 | 0.7311    | 0.7235 |
| 0.6164        | 0.8769 | 2500 | 0.5928          | 0.754    | 0.7545 | 0.7589    | 0.754  |
| 0.525         | 1.0523 | 3000 | 0.6018          | 0.75     | 0.7501 | 0.7510    | 0.75   |
| 0.5706        | 1.2276 | 3500 | 0.5946          | 0.7525   | 0.7535 | 0.7554    | 0.7525 |
| 0.5166        | 1.4030 | 4000 | 0.6254          | 0.753    | 0.7520 | 0.7540    | 0.753  |
| 0.5242        | 1.5784 | 4500 | 0.5979          | 0.741    | 0.7423 | 0.7452    | 0.741  |
| 0.4989        | 1.7538 | 5000 | 0.5992          | 0.754    | 0.7545 | 0.7560    | 0.754  |
| 0.5139        | 1.9291 | 5500 | 0.5917          | 0.755    | 0.7552 | 0.7564    | 0.755  |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4